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<div  id='write'  class = 'is-mac first-line-indent'><h3><a name="实验记录0318-resnet-mnist" class="md-header-anchor"></a><span>实验记录：0318 Resnet mnist</span></h3><p><span>2020·0318·22:30</span></p><hr /><pre spellcheck="false" class="md-fences md-end-block md-fences-with-lineno ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 34.74609375px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 31px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: -30.99609375px; width: 31px;"></div><div class="CodeMirror-gutter-wrapper CodeMirror-activeline-gutter" style="left: -30.99609375px;"><div class="CodeMirror-linenumber CodeMirror-gutter-elt CodeMirror-linenumber-show" style="left: 0px; width: 21px;">1</div></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> &nbsp;  Layer (type) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Output Shape &nbsp; &nbsp; &nbsp; &nbsp; Param #</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom-width: 0px; border-bottom-style: solid; border-bottom-color: transparent; top: 26px;"></div><div class="CodeMirror-gutters" style="height: 26px;"><div class="CodeMirror-gutter CodeMirror-linenumbers" style="width: 30px;"></div></div></div></div></pre><p><span>================================================================</span>
<span>            Conv2d-1           [-1, 64, 14, 14]           3,136</span>
<span>       BatchNorm2d-2           [-1, 64, 14, 14]             128</span>
<span>              ReLU-3           [-1, 64, 14, 14]               0</span>
<span>         MaxPool2d-4             [-1, 64, 7, 7]               0</span>
<span>            Conv2d-5             [-1, 64, 7, 7]          36,864</span>
<span>       BatchNorm2d-6             [-1, 64, 7, 7]             128</span>
<span>              ReLU-7             [-1, 64, 7, 7]               0</span>
<span>            Conv2d-8             [-1, 64, 7, 7]          36,864</span>
<span>       BatchNorm2d-9             [-1, 64, 7, 7]             128</span>
<span>             ReLU-10             [-1, 64, 7, 7]               0</span>
<span>       BasicBlock-11             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-12             [-1, 64, 7, 7]          36,864</span>
<span>      BatchNorm2d-13             [-1, 64, 7, 7]             128</span>
<span>             ReLU-14             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-15             [-1, 64, 7, 7]          36,864</span>
<span>      BatchNorm2d-16             [-1, 64, 7, 7]             128</span>
<span>             ReLU-17             [-1, 64, 7, 7]               0</span>
<span>       BasicBlock-18             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-19            [-1, 128, 4, 4]          73,728</span>
<span>      BatchNorm2d-20            [-1, 128, 4, 4]             256</span>
<span>             ReLU-21            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-22            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-23            [-1, 128, 4, 4]             256</span>
<span>           Conv2d-24            [-1, 128, 4, 4]           8,192</span>
<span>      BatchNorm2d-25            [-1, 128, 4, 4]             256</span>
<span>             ReLU-26            [-1, 128, 4, 4]               0</span>
<span>       BasicBlock-27            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-28            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-29            [-1, 128, 4, 4]             256</span>
<span>             ReLU-30            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-31            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-32            [-1, 128, 4, 4]             256</span>
<span>             ReLU-33            [-1, 128, 4, 4]               0</span>
<span>       BasicBlock-34            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-35            [-1, 256, 2, 2]         294,912</span>
<span>      BatchNorm2d-36            [-1, 256, 2, 2]             512</span>
<span>             ReLU-37            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-38            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-39            [-1, 256, 2, 2]             512</span>
<span>           Conv2d-40            [-1, 256, 2, 2]          32,768</span>
<span>      BatchNorm2d-41            [-1, 256, 2, 2]             512</span>
<span>             ReLU-42            [-1, 256, 2, 2]               0</span>
<span>       BasicBlock-43            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-44            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-45            [-1, 256, 2, 2]             512</span>
<span>             ReLU-46            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-47            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-48            [-1, 256, 2, 2]             512</span>
<span>             ReLU-49            [-1, 256, 2, 2]               0</span>
<span>       BasicBlock-50            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-51            [-1, 512, 1, 1]       1,179,648</span>
<span>      BatchNorm2d-52            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-53            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-54            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-55            [-1, 512, 1, 1]           1,024</span>
<span>           Conv2d-56            [-1, 512, 1, 1]         131,072</span>
<span>      BatchNorm2d-57            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-58            [-1, 512, 1, 1]               0</span>
<span>       BasicBlock-59            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-60            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-61            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-62            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-63            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-64            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-65            [-1, 512, 1, 1]               0</span>
<span>       BasicBlock-66            [-1, 512, 1, 1]               0</span></p><p><span>           Linear-67                   [-1, 10]           5,130</span></p><p><span>Total params: 11,175,370</span>
<span>Trainable params: 11,175,370</span></p><p><span>Non-trainable params: 0</span></p><p><span>Input size (MB): 0.00</span>
<span>Forward/backward pass size (MB): 1.08</span>
<span>Params size (MB): 42.63</span></p><p><span>Estimated Total Size (MB): 43.72</span></p><p><span>Traceback (most recent call last):</span>
<span>  File &quot;myresnet.py&quot;, line 170, in </span><module>
<span>    %(epoch+1, NUM_EPOCHS, batch_idx,</span>
<span>NameError: name &#39;NUM_EPOCHS&#39; is not defined</span>
<span>(python36) [zhangkaiwang@m7-model-gpu15 resnet18]$ python myresnet.py</span>
<span>Image batch dimensions: torch.Size([128, 1, 28, 28])</span>
<span>Image label dimensions: torch.Size([128])</span>
<span>Epoch: 1 | Batch index: 0 | Batch size: 128</span></p><p><span>Epoch: 2 | Batch index: 0 | Batch size: 128</span></p><pre spellcheck="false" class="md-fences md-end-block md-fences-with-lineno ty-contain-cm modeLoaded" lang=""><div class="CodeMirror cm-s-inner CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 0px; left: 34.74609375px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 31px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre><div class="CodeMirror-linenumber CodeMirror-gutter-elt"><div>1</div></div></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: -30.99609375px; width: 31px;"></div><div class="CodeMirror-gutter-wrapper CodeMirror-activeline-gutter" style="left: -30.99609375px;"><div class="CodeMirror-linenumber CodeMirror-gutter-elt CodeMirror-linenumber-show" style="left: 0px; width: 21px;">1</div></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;"> &nbsp;  Layer (type) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Output Shape &nbsp; &nbsp; &nbsp; &nbsp; Param #</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom-width: 0px; border-bottom-style: solid; border-bottom-color: transparent; top: 26px;"></div><div class="CodeMirror-gutters" style="height: 26px;"><div class="CodeMirror-gutter CodeMirror-linenumbers" style="width: 30px;"></div></div></div></div></pre><p><span>================================================================</span>
<span>            Conv2d-1           [-1, 64, 14, 14]           3,136</span>
<span>       BatchNorm2d-2           [-1, 64, 14, 14]             128</span>
<span>              ReLU-3           [-1, 64, 14, 14]               0</span>
<span>         MaxPool2d-4             [-1, 64, 7, 7]               0</span>
<span>            Conv2d-5             [-1, 64, 7, 7]          36,864</span>
<span>       BatchNorm2d-6             [-1, 64, 7, 7]             128</span>
<span>              ReLU-7             [-1, 64, 7, 7]               0</span>
<span>            Conv2d-8             [-1, 64, 7, 7]          36,864</span>
<span>       BatchNorm2d-9             [-1, 64, 7, 7]             128</span>
<span>             ReLU-10             [-1, 64, 7, 7]               0</span>
<span>       BasicBlock-11             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-12             [-1, 64, 7, 7]          36,864</span>
<span>      BatchNorm2d-13             [-1, 64, 7, 7]             128</span>
<span>             ReLU-14             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-15             [-1, 64, 7, 7]          36,864</span>
<span>      BatchNorm2d-16             [-1, 64, 7, 7]             128</span>
<span>             ReLU-17             [-1, 64, 7, 7]               0</span>
<span>       BasicBlock-18             [-1, 64, 7, 7]               0</span>
<span>           Conv2d-19            [-1, 128, 4, 4]          73,728</span>
<span>      BatchNorm2d-20            [-1, 128, 4, 4]             256</span>
<span>             ReLU-21            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-22            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-23            [-1, 128, 4, 4]             256</span>
<span>           Conv2d-24            [-1, 128, 4, 4]           8,192</span>
<span>      BatchNorm2d-25            [-1, 128, 4, 4]             256</span>
<span>             ReLU-26            [-1, 128, 4, 4]               0</span>
<span>       BasicBlock-27            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-28            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-29            [-1, 128, 4, 4]             256</span>
<span>             ReLU-30            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-31            [-1, 128, 4, 4]         147,456</span>
<span>      BatchNorm2d-32            [-1, 128, 4, 4]             256</span>
<span>             ReLU-33            [-1, 128, 4, 4]               0</span>
<span>       BasicBlock-34            [-1, 128, 4, 4]               0</span>
<span>           Conv2d-35            [-1, 256, 2, 2]         294,912</span>
<span>      BatchNorm2d-36            [-1, 256, 2, 2]             512</span>
<span>             ReLU-37            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-38            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-39            [-1, 256, 2, 2]             512</span>
<span>           Conv2d-40            [-1, 256, 2, 2]          32,768</span>
<span>      BatchNorm2d-41            [-1, 256, 2, 2]             512</span>
<span>             ReLU-42            [-1, 256, 2, 2]               0</span>
<span>       BasicBlock-43            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-44            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-45            [-1, 256, 2, 2]             512</span>
<span>             ReLU-46            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-47            [-1, 256, 2, 2]         589,824</span>
<span>      BatchNorm2d-48            [-1, 256, 2, 2]             512</span>
<span>             ReLU-49            [-1, 256, 2, 2]               0</span>
<span>       BasicBlock-50            [-1, 256, 2, 2]               0</span>
<span>           Conv2d-51            [-1, 512, 1, 1]       1,179,648</span>
<span>      BatchNorm2d-52            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-53            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-54            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-55            [-1, 512, 1, 1]           1,024</span>
<span>           Conv2d-56            [-1, 512, 1, 1]         131,072</span>
<span>      BatchNorm2d-57            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-58            [-1, 512, 1, 1]               0</span>
<span>       BasicBlock-59            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-60            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-61            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-62            [-1, 512, 1, 1]               0</span>
<span>           Conv2d-63            [-1, 512, 1, 1]       2,359,296</span>
<span>      BatchNorm2d-64            [-1, 512, 1, 1]           1,024</span>
<span>             ReLU-65            [-1, 512, 1, 1]               0</span>
<span>       BasicBlock-66            [-1, 512, 1, 1]               0</span></p><p><span>Linear-67                   [-1, 10]           5,130</span></p><p><span>Total params: 11,175,370</span>
<span>Trainable params: 11,175,370</span></p><p><span>Non-trainable params: 0</span></p><p><span>Input size (MB): 0.00</span>
<span>Forward/backward pass size (MB): 1.08</span>
<span>Params size (MB): 42.63</span></p><p><span>Estimated Total Size (MB): 43.72</span></p><p><span>Epoch: 001/010 | Batch 0000/0469 | Cost: 2.4471</span>
<span>Epoch: 001/010 | Batch 0050/0469 | Cost: 0.0794</span>
<span>Epoch: 001/010 | Batch 0100/0469 | Cost: 0.1959</span>
<span>Epoch: 001/010 | Batch 0150/0469 | Cost: 0.2234</span>
<span>Epoch: 001/010 | Batch 0200/0469 | Cost: 0.1466</span>
<span>Epoch: 001/010 | Batch 0250/0469 | Cost: 0.0707</span>
<span>Epoch: 001/010 | Batch 0300/0469 | Cost: 0.2032</span>
<span>Epoch: 001/010 | Batch 0350/0469 | Cost: 0.0515</span>
<span>Epoch: 001/010 | Batch 0400/0469 | Cost: 0.0456</span>
<span>Epoch: 001/010 | Batch 0450/0469 | Cost: 0.1236</span>
<span>Epoch: 001/010 | Train: 98.042%</span>
<span>Time elapsed: 0.72 min</span>
<span>Epoch: 002/010 | Batch 0000/0469 | Cost: 0.0186</span>
<span>Epoch: 002/010 | Batch 0050/0469 | Cost: 0.0141</span>
<span>Epoch: 002/010 | Batch 0100/0469 | Cost: 0.0558</span>
<span>Epoch: 002/010 | Batch 0150/0469 | Cost: 0.0507</span>
<span>Epoch: 002/010 | Batch 0200/0469 | Cost: 0.0244</span>
<span>Epoch: 002/010 | Batch 0250/0469 | Cost: 0.0421</span>
<span>Epoch: 002/010 | Batch 0300/0469 | Cost: 0.0517</span>
<span>Epoch: 002/010 | Batch 0350/0469 | Cost: 0.0915</span>
<span>Epoch: 002/010 | Batch 0400/0469 | Cost: 0.0398</span>
<span>Epoch: 002/010 | Batch 0450/0469 | Cost: 0.0630</span>
<span>Epoch: 002/010 | Train: 99.123%</span>
<span>Time elapsed: 1.43 min</span>
<span>Epoch: 003/010 | Batch 0000/0469 | Cost: 0.0102</span>
<span>Epoch: 003/010 | Batch 0050/0469 | Cost: 0.0206</span>
<span>Epoch: 003/010 | Batch 0100/0469 | Cost: 0.0652</span>
<span>Epoch: 003/010 | Batch 0150/0469 | Cost: 0.0203</span>
<span>Epoch: 003/010 | Batch 0200/0469 | Cost: 0.0252</span>
<span>Epoch: 003/010 | Batch 0250/0469 | Cost: 0.1252</span>
<span>Epoch: 003/010 | Batch 0300/0469 | Cost: 0.0362</span>
<span>Epoch: 003/010 | Batch 0350/0469 | Cost: 0.0165</span>
<span>Epoch: 003/010 | Batch 0400/0469 | Cost: 0.0699</span>
<span>Epoch: 003/010 | Batch 0450/0469 | Cost: 0.2126</span>
<span>Epoch: 003/010 | Train: 98.872%</span>
<span>Time elapsed: 2.15 min</span>
<span>Epoch: 004/010 | Batch 0000/0469 | Cost: 0.0051</span>
<span>Epoch: 004/010 | Batch 0050/0469 | Cost: 0.0108</span>
<span>Epoch: 004/010 | Batch 0100/0469 | Cost: 0.0157</span>
<span>Epoch: 004/010 | Batch 0150/0469 | Cost: 0.0337</span>
<span>Epoch: 004/010 | Batch 0200/0469 | Cost: 0.0489</span>
<span>Epoch: 004/010 | Batch 0250/0469 | Cost: 0.0286</span>
<span>Epoch: 004/010 | Batch 0300/0469 | Cost: 0.0057</span>
<span>Epoch: 004/010 | Batch 0350/0469 | Cost: 0.0065</span>
<span>Epoch: 004/010 | Batch 0400/0469 | Cost: 0.0052</span>
<span>Epoch: 004/010 | Batch 0450/0469 | Cost: 0.0078</span>
<span>Epoch: 004/010 | Train: 99.170%</span>
<span>Time elapsed: 2.87 min</span>
<span>Epoch: 005/010 | Batch 0000/0469 | Cost: 0.0249</span>
<span>Epoch: 005/010 | Batch 0050/0469 | Cost: 0.0530</span>
<span>Epoch: 005/010 | Batch 0100/0469 | Cost: 0.0053</span>
<span>Epoch: 005/010 | Batch 0150/0469 | Cost: 0.0095</span>
<span>Epoch: 005/010 | Batch 0200/0469 | Cost: 0.0411</span>
<span>Epoch: 005/010 | Batch 0250/0469 | Cost: 0.0113</span>
<span>Epoch: 005/010 | Batch 0300/0469 | Cost: 0.0020</span>
<span>Epoch: 005/010 | Batch 0350/0469 | Cost: 0.0192</span>
<span>Epoch: 005/010 | Batch 0400/0469 | Cost: 0.0531</span>
<span>Epoch: 005/010 | Batch 0450/0469 | Cost: 0.0302</span>
<span>Epoch: 005/010 | Train: 99.140%</span>
<span>Time elapsed: 3.60 min</span>
<span>Epoch: 006/010 | Batch 0000/0469 | Cost: 0.0081</span>
<span>Epoch: 006/010 | Batch 0050/0469 | Cost: 0.0146</span>
<span>Epoch: 006/010 | Batch 0100/0469 | Cost: 0.0309</span>
<span>Epoch: 006/010 | Batch 0150/0469 | Cost: 0.0188</span>
<span>Epoch: 006/010 | Batch 0200/0469 | Cost: 0.0309</span>
<span>Epoch: 006/010 | Batch 0250/0469 | Cost: 0.0264</span>
<span>Epoch: 006/010 | Batch 0300/0469 | Cost: 0.0502</span>
<span>Epoch: 006/010 | Batch 0350/0469 | Cost: 0.0055</span>
<span>Epoch: 006/010 | Batch 0400/0469 | Cost: 0.0011</span>
<span>Epoch: 006/010 | Batch 0450/0469 | Cost: 0.0033</span>
<span>Epoch: 006/010 | Train: 99.452%</span>
<span>Time elapsed: 4.31 min</span>
<span>Epoch: 007/010 | Batch 0000/0469 | Cost: 0.0266</span>
<span>Epoch: 007/010 | Batch 0050/0469 | Cost: 0.0021</span>
<span>Epoch: 007/010 | Batch 0100/0469 | Cost: 0.0042</span>
<span>Epoch: 007/010 | Batch 0150/0469 | Cost: 0.0095</span>
<span>Epoch: 007/010 | Batch 0200/0469 | Cost: 0.0050</span>
<span>Epoch: 007/010 | Batch 0250/0469 | Cost: 0.0252</span>
<span>Epoch: 007/010 | Batch 0300/0469 | Cost: 0.0019</span>
<span>Epoch: 007/010 | Batch 0350/0469 | Cost: 0.0045</span>
<span>Epoch: 007/010 | Batch 0400/0469 | Cost: 0.0083</span>
<span>Epoch: 007/010 | Batch 0450/0469 | Cost: 0.0076</span>
<span>Epoch: 007/010 | Train: 99.603%</span>
<span>Time elapsed: 5.02 min</span>
<span>Epoch: 008/010 | Batch 0000/0469 | Cost: 0.0010</span>
<span>Epoch: 008/010 | Batch 0050/0469 | Cost: 0.0081</span>
<span>Epoch: 008/010 | Batch 0100/0469 | Cost: 0.0009</span>
<span>Epoch: 008/010 | Batch 0150/0469 | Cost: 0.0310</span>
<span>Epoch: 008/010 | Batch 0200/0469 | Cost: 0.0130</span>
<span>Epoch: 008/010 | Batch 0250/0469 | Cost: 0.0183</span>
<span>Epoch: 008/010 | Batch 0300/0469 | Cost: 0.0025</span>
<span>Epoch: 008/010 | Batch 0350/0469 | Cost: 0.0068</span>
<span>Epoch: 008/010 | Batch 0400/0469 | Cost: 0.0132</span>
<span>Epoch: 008/010 | Batch 0450/0469 | Cost: 0.0028</span>
<span>Epoch: 008/010 | Train: 99.585%</span>
<span>Time elapsed: 5.73 min</span>
<span>Epoch: 009/010 | Batch 0000/0469 | Cost: 0.0174</span>
<span>Epoch: 009/010 | Batch 0050/0469 | Cost: 0.0088</span>
<span>Epoch: 009/010 | Batch 0100/0469 | Cost: 0.0075</span>
<span>Epoch: 009/010 | Batch 0150/0469 | Cost: 0.0003</span>
<span>Epoch: 009/010 | Batch 0200/0469 | Cost: 0.0020</span>
<span>Epoch: 009/010 | Batch 0250/0469 | Cost: 0.0039</span>
<span>Epoch: 009/010 | Batch 0300/0469 | Cost: 0.0118</span>
<span>Epoch: 009/010 | Batch 0350/0469 | Cost: 0.0083</span>
<span>Epoch: 009/010 | Batch 0400/0469 | Cost: 0.0407</span>
<span>Epoch: 009/010 | Batch 0450/0469 | Cost: 0.0061</span>
<span>Epoch: 009/010 | Train: 99.630%</span>
<span>Time elapsed: 6.45 min</span>
<span>Epoch: 010/010 | Batch 0000/0469 | Cost: 0.0375</span>
<span>Epoch: 010/010 | Batch 0050/0469 | Cost: 0.0106</span>
<span>Epoch: 010/010 | Batch 0100/0469 | Cost: 0.0027</span>
<span>Epoch: 010/010 | Batch 0150/0469 | Cost: 0.0111</span>
<span>Epoch: 010/010 | Batch 0200/0469 | Cost: 0.0048</span>
<span>Epoch: 010/010 | Batch 0250/0469 | Cost: 0.0053</span>
<span>Epoch: 010/010 | Batch 0300/0469 | Cost: 0.0156</span>
<span>Epoch: 010/010 | Batch 0350/0469 | Cost: 0.0050</span>
<span>Epoch: 010/010 | Batch 0400/0469 | Cost: 0.0043</span>
<span>Epoch: 010/010 | Batch 0450/0469 | Cost: 0.0264</span>
<span>Epoch: 010/010 | Train: 99.665%</span>
<span>Time elapsed: 7.14 min</span>
<span>Total Training Time: 7.14 min</span>
<span>Test accuracy: 99.09%</span></p></div>
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